Principal MLE @ A16Z Backed Security Startup | $200 - $250k + Equity
$200,000–$250,000 year
HybridNew York City, New York, United States or New York, United States
New York City, New York, United States or New York, United StatesHybridFull Time$200,000–$250,000 yearSenior LevelSmall
Full TimeSenior LevelSmall
Job Summary
Build and own training pipelines for data preparation, reproducible fine-tuning, and release automation. Construct evaluation infrastructure including automated eval runs, regression gates, and dataset versioning. Own model serving in production by managing low-latency inference, batching, autoscaling, and cost optimization. Ship model updates safely using versioning, canarying, and drift monitoring while turning expert labels into clean training data. Set the bar for ML infrastructure as the team grows on a small, senior team focused on industrial-grade systems.
Required Qualifications
- 8+ years of software engineering experience
- 4+ years building infrastructure for ML or LLM systems in production
- Hands-on depth with the modern LLM stack: PyTorch, distributed training, fine-tuning at scale (LoRA, SFT), and inference engines such as vLLM or TensorRT-LLM
- Experience building eval harnesses, regression gates, or dataset pipelines
- Solid understanding of precision, recall, and calibration
- Production mindset — you have owned model serving with real latency, reliability, and cost constraints
- Strong fundamentals: Python, containers, CI/CD, cloud infrastructure, observability
- High ownership on a small team: scope your own work, ship weekly, make pragmatic build-vs-buy calls
- You enjoy being the engineering counterpart to a research partner — tight collaboration, clear interfaces, no turf wars
- Location: New York, NY (hybrid — 3 days/week in office)
Desired Qualifications
- Experience productionizing small or specialized language models
- Experience with structured-output serving or constrained decoding in production
- Prior work in a regulated or high-stakes domain such as fintech, healthcare, legal, or trust and safety
- Experience deploying models into customer-controlled environments
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